Abstract
In this paper, we apply a special application of the Rao quadratic diversity for multiscale analysis of land use changes in a mixed agricultural-forest landscape in Central Italy. The proposed approach is similar to a block-size analysis of compositional diversity for which a given landscape is overlaid with a series of square grids composed of increasingly larger boxes. The combination of land cover classes in each box is recorded, and the Rao quadratic diversity is computed for the frequency distribution of the land cover classes at each box-size. Plotting compositional diversity versus box-size provides information on the scale-dependent pattern of the landscape. Since the proposed methodology is not severely influenced by the co-registration accuracy of the underlying data sets, it may prove to be reasonably adequate for analyzing historical data sets of varying resolution and quality, like aerial photographs or categorical maps.
Highlights
It has been widely recognized that landscape structure is spatially correlated and scale-dependent; that is, it changes with the scale of observation [1]
A potential weakness related to the use of traditional information-theoretic measures for computing landscape structure is that these measures treat all land cover classes (LCCs) or combinations of them as distinct, where the meaning of “ distinct” will be clear below
In this paper, we show that scale-dependent landscape structure can be adequately summarized with a particular application of
Summary
It has been widely recognized that landscape structure is spatially correlated and scale-dependent; that is, it changes with the scale of observation [1]. A potential weakness related to the use of traditional information-theoretic measures for computing landscape structure is that these measures treat all land cover classes (LCCs) or combinations of them (see, e.g., [5,7]) as distinct, where the meaning of “ distinct” will be clear below.
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